This study attempts to come up with machine system that outperforms the radiologist in extracting tissue structure information from ultrasound B- scan images of the breast. As such, it aims at the development of a system that processes the ultrasound signal of the breast and gives quantitative information about parameters that define the different pathologies of the breast tissue, based on modeling the back-scatter echo. The goal is to develop a system that extracts more information than the one available by the visual inspection of the B-scans of the breast which is limited due to the masking effect of the speckle. Mathematical models that are consistent with the tissue micro-structure of the breast and take under consideration the image formation process will be derived, and relationships between the different kinds of scatterers and their sonic echo will be established. Such an approach has been successfully applied to liver scans when examining diffuse diseases, which alter the regular lobular structure of the liver. The models are based on the World decomposition of the US signal into components that will be modeled according their specific properties. The model incorporates both first order statistics (intensity histogram) as well as higher statistics (texture histogram). The first order statistics answer questions as whether a shadow in a mammogram corresponds to a cyst or a solid lesions while the higher order statistics shed light on structural anomalies of the tissue which indicate the first stage of cancer (ductal carcinoma in situ). The proposed techniques will be applied on real data and the results will be compared with the histological findings.